The invention pertains to digital data processing and, more particularly, to methods of analyzing digital data representing interactions to identify those involving distinct individuals and/or the entities with which they are affiliated (e.g., households, businesses, social or other groups).
The invention has application, by way of non-limiting example, in identifying among data records reflecting interactions between, say, a retailer and the public, those records reflecting interactions with a given person and/or his or her household, business or social colleagues—regardless of whether those interactions are in the retailer's brick-and-mortar stores, through the mails, over cyberspace or otherwise.
The invention also has application, by way of further non-limiting example, in identifying such interactions in which a common device—such as, for example, an NFC-enabled mobile phone—is utilized, e.g., by an individual, a household member or colleague when visiting a retailer's brick-and-mortar store, browsing its websites, etc.
Tracking, analyzing and reporting interactions between an individual and an entity can be difficult. Take the case of interactions with a retailer and, specifically, for example, purchases made by the individual from a large retailer. The individual might use checks for some transactions at the retailer's brick-and-mortar stores, yet, cash for others. Identifying information gleaned from checks and stored to the retailer's back office databases, along with records of the attendant purchases, can be useful in targeting subsequent adverts to the individual. However, no such information is typically available for the cash purchases—which, for all intents and purposes, are anonymous—and, hence, cannot be taken into account in the targeting the buyer.
The problem is compounded by the retailer's web sites and call centers, since payments by the individual to these may be by still other means (e.g., credit card, Paypal, etc.). While information gleaned from those transactions might be useful in targeting advertising to the buyer in connection with his or her online purchases, it may be difficult to match with information about in-store purchases.
And, as if correlating various purchases made by an individual were not hard enough, worse still is associating them with the broader class of interactions he/she may have with the retailer. Interactions which may include, for example, visits by the individual to the retailer's sites in the real or cyber worlds; customer service calls and other contacts (e.g., carry-in and in-home repairs); personalized mailings, whether in paper or electronic; and, mass-media marketing campaigns in newspapers, television, radio, or billboards in his/her area.
Extend this to groups or organizations of which the individual is a member—say, his/her household, company, or social clubs—and the problem is more difficult. In the case of interactions between a retailing entity and members of a household, for example, touch points may differ in time, place and nature. Likewise, interactions with actual and potential corporate purchasers may come from any of a diverse number of employees, as well as from the purchasing or other department(s). Those with members of a common house of worship and other social organization may be still more extenuated.
Of course, these problems are not limited to retailers. Other for-profit entities, from manufacturers to publishers, as well as nonprofits and even governmental organizations face difficulty, too, in tracking, analyzing and reporting interactions with individuals, households, social groups, etc.—whether for customizing fund-raising (e.g., in the case of nonprofits), gauging market trends and the impact of marketing (e.g., in the case of for-profits) and understanding needs of their constituencies (e.g., in the case of governmental entities).
In an attempt to get a better handle on this, many entities employ a manual or electronic registration log, asking visitors to sign in with identifying and residence information, identification cards, user IDs, or the like before every interaction. Retailers have their own form of these, namely, loyalty cards—but, more traditionally, they rely on identifying data collected at the time of sale. Examples are credit card account numbers, discount numbers, phone numbers and so forth. Thus, for example, a retailer seeking to launch a targeted ad campaign at households might cull its retail transaction database by credit card number, using purchases made under each separate number to drive customized paper mailings, e-mailings, and so forth.
Unfortunately, attributes collected in registration logs, at point of sale and the like are not always accurate. In retailing, this can be accidental or due to willful action on the part of the customer who provides the information and/or the cashier or salesperson who records it. For example, the head of a household might mistakenly give an office phone number—or, perhaps, an apocryphal phone number—when making a purchase for the household. By way of further example, a cashier may enter his or her own air miles account number in order to get personal credit for a purchase by a customer.
Moreover, even those attributes that are accurately recorded can be over- and under-inclusive when it comes to distinguishing buying groups. Thus, while most members of a household might rely on a single credit card in making a majority of purchases, the head of the household may use any of several different credit cards, thus, blurring efforts to tie all of those purchases back to the same family. On the other end of the spectrum, purchases made under a discount code assigned to a club may imply relationships between purchasers and purchases that, in fact, do not exist.
In view of the foregoing, an object of the invention is to provide improved systems, apparatus and methods of digital data processing and, particularly, for example, of analyzing data representing interactions to identify those with a particular individual and/or the household, business, social or other group to which he/she belongs.
A related object is to provide such systems, apparatus and methods as facilitate identifying, among data records reflecting interactions between the public and a for-profit, nonprofit, governmental or other entity, those records reflecting interactions with given individual and/or an entity with which he/she is affiliated, whether that is a household, business or social group. A further related object of the invention is to provide such systems, apparatus and methods as facilitate identifying those records, regardless of whether the underlying interactions occur in the real world, in cyberspace or otherwise.
A further related object of the inventions is to provide such systems, apparatus and methods as can be applied in identifying purchase transactions between a retailing entity and an individual and/or her/her family, business or social colleagues.
A related object is to provide such methods as facilitate identifying such interactions notwithstanding errors in, for example, email addresses, phone numbers, air miles account numbers, etc., provided in connection with the transactions.